Related
I posted a question earlier about how notifications, and users seeing those notifications, could be modelled in DDD.
Link Here: Does everything have to be an aggregate? Many-to-Many Link
For a brief summary of this:
We can raise notifications in the system that we want to show users.
(A notification will target certain users, which you define a filter for. E.g. only show admins, only show normal users, only show users for x client)
When a user sees a notification we want to mark it so they don't see it again.
A suggestion was made on the post to have a notification aggregate and store the reference to it in the User aggregate.
So when a notification is created, the event will be picked up, and a service will add that notification to the users it seems fit.
So we have a notification list in the user.
I think this is a bounded context (a notification bounded context). Certainly if i was modelling it as a microservice, I would handle this notifications stuff in its own microservice.
If we were to use microservices, the user created event would come from another service (a users service).
Question:
The notification creation would go in the notifications microservice. I would also be tempted to put the user marking that they have seen a notification in that service as well.
So at this point, the notifications microservice wouldn't hold a full aggregate of a user, it would only have a partial, containing the; id, collection of notifications and any criteria might want to filter from
Is it ok to have an aggregate (be it a partial one, as it is only the stuff we want) in a microservice (notification microservice) that it isn't owned by?
So essentially we have an aggregate of the user in the users microservice and the notifications one.
This doesn't sound too bad as it is going to reduce the footprint on the user, by splitting parts up and it is nice to bundle this functionality into the service that handles it.
However do we want to keep all the user stuff in one place? even it it puts other functionality in with it? Would the seeing of a notification go in the user microservice (that feels wrong)
Thanks
In short, don't share the aggregates, share projections of those aggregates, which are value objects representing aggregates from other bounded contexts.
Is it ok to have an aggregate (be it a partial one, as it is only the stuff we want) in a microservice (notification microservice) that it isn't owned by?
What you call a "partial aggregate" I would call a projection of an aggregate owned by a separate BC. So this projection can be owned by the BC exposed in the importing microservice. In this sense, yes, it is ok.
So essentially we have an aggregate of the user in the users microservice and the notifications one.
No, you don't. You have User in its BC and you have a projection of the User in the Notifications BC. A BC can own the projections of aggregates from other BCs but not the foreign aggregates themselves. You only want to project what you need from the other BCs, not everything. If it was everything, then you've pretty much broken some fundamental DDD. And from a physical perspective, you might be tempted to share databases and so on, which defeats some of the hallmarks of good microservice architecture.
Question: The notification creation would go in the notifications microservice. I would also be tempted to put the user marking that they have seen a notification in that service as well.
I think this would be OK. It sounds like it from the context of your question (I certainly don't know the whole of what you're doing). Perhaps in your Notifications BC, you have a NotifiedUser with a list of Notifications or perhaps it's the other way around SeenNotifications with a list of Users.
I am working on a desktop application that requires synchronization between several clients. Basically, a group of people (let's say between 2 and 10) all run the same application. One of them hosts a server and the other clients connect to that server. The client that hosts the server also connects to his own server.
The applications should stay synchronized between all clients, meaning all clients see the same data in the application. Specifically, the data in question I can define in two separate forms:
A simple property with a certain value (this value must stay synchronized)
A list of properties (the items in the list and their values must stay synchronized)
Simple examples of (1) could be: which item in a list does the client currently have selected, and what's the current location of the client's mouse pointer within the application window. These properties keep changing continuously but the number of these properties is constant and does not grow (e.g. defined during design time).
An example of (2) could be a list of chat messages. These lists will grow during runtime with no way to predict how many items there will be.
Here is an example code in C# for the state, client and chat messages:
public class State
{
// A single value shared between all clients
public int SimpleInteger {get;set;}
// List of connected clients and their individual states
public List<Client> Clients {get;set;}
// List of chat messages
public List<ChatMessage> Messages {get;set;}
}
public class Client
{
public string ClientId {get;set;}
public string Username {get;set;}
public ClientState ClientState {get;set;}
}
public class ClientState
{
public string ClientId {get;set;}
public int SelectedIndex {get;set;}
public int MouseX {get;set;}
public int MouseY {get;set;}
}
public class ChatMessage
{
public string ClientId {get;set;}
public string Message {get;set;}
}
I've been working on this on and off for a long time but whatever kind of state synchronization I came up with, it never worked well.
When I search for solutions, I only ever find solutions for games, but those are not very helpful because my requirements are different:
I cannot deal with "dropped updates", I cannot predict (interpolate or extrapolate) what the other clients are doing. Every client needs to receive every update to stay in sync.
On the other hand, I don't care about lag (within reason). It is fine if I see the updates of other client with about a second delay.
When a new client connects (or reconnects), a large portion of the state must be transfered (for example: the list of chat messages from example 2). Each client is required to know about the entire history of the chat so this must be downloaded when a client connects.
My current solution can be summarized as follows:
The server keeps track of the state, e.g. the source of truth.
The state contains the properties that require synchronizing.
The state also contains a list of connected users (and their usernames etc).
Clients also each keep a local copy of the state, which they can act upon immediately. For example, they update their mouse position in their local state continously.
Whenever a client updates his local state, this update is sent to the server.
Potential exceptions here are things that change too fast such as the mouse position, those I will only send in regular intervals.
The server also updates the common "source of truth" state.
Finally, the server updates all other clients with the new updated state.
The last two steps are where I'm struggling. I can think of two methods to synchronize the state, one is easy but probably not efficient and the other is efficient but prone to errors.
The server simply sends the entire state to all clients.
As soon as the server receives an update from the client, the update is applied to the state and the new state is broadcasted. Every other client replaces their local state.
I feel this will probably work, but the state can grow in size quickly due to the "list" items (for example chat messages). In my previous attempts, this quickly became a problem and sending the state back become much too slow.
The server re-sends the same update (that it received) to all other clients.
Each client then only applies the new update to their state locally to sync back with the server.
This is probably much more efficient and sending the entire state is only necessary when a client connects.
However, in the past I frequently ran into desync issues where clients were no longer in sync. I don't really know what caused it, probably conflicts between messages (for example server telling the client to update a value in the state, but the client just updated his local value, which has precedence?). After this happens, everything went completely wrong as the updates are now being applied to two different states and have different outcomes.
I'm looking for some guidance on general concepts on how to achieve this. I'm using several messaging libraries to achieve the actual communication between client and server and that part is not an issue I think. I can make sure in these libraries that every message is received for example (though I'm not sure if the order is guaranteed). Like I said before, lag is not an issue, but I must guarantee every state update is received both by the server and by every other client.
Any help would be great! Thanks.
This is a hard problem and there are enough tricky areas that I wouldn't want to build this myself. Authentication, conflicting updates, API management, network outages, single point of failure, and local persistence come to mind.
If you're up for using a cloud-based solution, Google Cloud Firestore takes care of those tricky areas and does what you need:
Clients save data to the database, by creating, updating, or deleting records. Example code.
Whenever a record is created, updated, or deleted, all clients get realtime notifications. Example code.
(After you follow the links above, make sure you click C# above the code boxes to see the C# code).
This is a complicated issue, with many moving parts, as you seem to understand. As I've been researching this, I've read a couple comments on questions like this one on a variety of Q&A sites, stating this kind of thing is a project all on it's own.
Disclaimer: I haven't done this myself, so I don't know how well this would work, but maybe you can take my suggestions and work with them, if you haven't already done so. I've worked on projects where this was implemented, but I wasn't part of that implementation directly.
Connection
Since you haven't said which library you are using for the connection, I'm going to assume you are using websockets or something similar. If not, I suggest you move to something like websockets. It allows for a (near) constant connection between client and server so that data can be pushed both directions, avoiding the client from having to poll and pull the data. The link below seems to have a decent walk-though on how to do it, so I won't try to. Because links die, here's the first example code they give, which seems pretty simple.
​using System.Net.Sockets;
using System.Net;
using System;
class Server {
public static void Main() {
TcpListener server = new TcpListener(IPAddress.Parse("127.0.0.1"), 80);
server.Start();
Console.WriteLine("Server has started on 127.0.0.1:80.{0}Waiting for a connection...", Environment.NewLine);
TcpClient client = server.AcceptTcpClient();
Console.WriteLine("A client connected.");
}
}
https://developer.mozilla.org/en-US/docs/Web/API/WebSockets_API/Writing_WebSocket_server
Client start up
Once you have a stable connection between server and client, you need to make sure the data is in sync. When the user starts the app, you can get the timestamp of the latest change in each table and compare that to the server. If they are exactly the same, you have a somewhat reasonable expectation that the table hasn't changed. I'm assuming each table has a column containing the timestamp for the last edit made to the row.
For the tables that have changed, you can have the server send the new and updated rows to the client based on the client's "last changed timestamp".
Since the internet isn't 100% guaranteed to be connected, you will also need to keep track of the times the client has been connected vs. when they've been on the app (unless the app just won't work without being connected to the server). This information also needs to be sent to the server to compare to data changed during intervals where the client hasn't been connected.
Once timestamp matching has been done, you need to compare the row counts. If they match, you can more reasonably assume the tables are the same. If they aren't, you can see about matching ID/primary keys. There's a variety of different ways to do this, including 1:1 matching (which is slowest but most reliable), or you can do some math with the IDs (assuming numerical IDs) and try to see what's different in batches of 100 rows (for example). Idea: If adding the sorted, auto-increment integer IDs for the first 100 rows is the same on the client and the server, all those rows exist on both servers, but if it doesn't match, you can try the 1:1 match to see what's missing. Because this can be lengthy for large databases, you may want to track this type of sync in another table, so it doesn't need to be done all the time.
Instead, you may want a table to track all the data not sent to a client. This would require a confirmation that the data sent was correctly inserted into the client DB. This could also work on the client side to track what hasn't been sent to the server. Of course, this kind of thing can get cumbersome quickly, even if you're just tracking keys, table names, and timestamps. You can rack up millions of rows quickly, if you don't remove old data periodically. This is why I suggest tracking unsent data, so that anything that becomes "sent" is no longer tracked by this table and removed.
If you don't want to code and manage all that, you can try for a library that does it. There are a variety out there. Even Microsoft has one, but it's on extended support to only 1/1/2021. What happens after that, I doubt even Microsoft knows, but it gets you 1.25 years to come up with a different solution.
Creating Synchronization Providers With The Sync Framework
The Sync Framework can be used to build apps that synchronize data from any data store using any protocol over a network. We'll show you how it works and get you started building a custom sync provider.
https://learn.microsoft.com/en-us/previous-versions/sql/synchronization/mt490616(v=msdn.10)
https://support.microsoft.com/en-us/lifecycle/search?alpha=Microsoft%20Sync%20Framework%202.1
Normal runtime
Once you have your data synced on startup (or in the background after startup), you can simply send the data to the server normally, as in when the user makes changes. Since you'll have a websocket type connection, any changes the server gets from other clients will be able to be pushed to all the other clients.
As far as changing the data in real time in your app, you may have to be constantly polling your local/client DB for timestamp changes so the UI can be appropriately updated. There may be something within C# that does this for you or another library you can find.
Conclusion
At this point, I'm out of ideas. It seems reasonable to me this would work, even though it's a lot of work. Hopefully you can take what I have and use it as a foundation to your own ideas on how to accomplish your task. It seems there's a lot of work ahead of you, so good luck!
Footnote
As I'm currently the only answer after several days of it being unanswered, I'm going to assume no one else has anything better to suggest. If they do, I'd encourage them to make their own answer instead of complaining about mine. People tweaking this answer is expected, but please remember community standards when making comments.
I'm only answering this because I haven't seen anyone else do it on this or other sites. It's only been bits and disconnected pieces here & there, with people still not being able to make sense of it as a whole.
This and similar questions have been asked before on this site and closed as "too broad". If you feel this same way as a reader, please vote so on the Question not this answer.
There are several solutions to your problem.
You could use a BizTalk server out-of-the box. This may not be what you have in mind.
If you want something more home-brewed, you could use WCF (Windows Communication Foundation) with MSMQ (Microsoft Message Queue). This would give you guaranteed message delivery, and durable messages (if you want). You would not have to worry about lost connections, and other errors occurring during messages transmission.
You can go down another level and use direct TCP and UDP protocols to transmit messages. But now, you have to take care of more error cases.
Any SQL DBMS implements one important part of your problem statement: it maintains shared state. Consider what ACID promises:
Consistency. At any one instant, all clients reading from the database are guaranteed to see the same information.
Atomicity. The client updating the database can use as many steps as needed. When the transaction is committed, the data are changed entirely or not at all.
Isolation. The server gives each client the illusion of interacting with it alone. It handles concurrent updates, and updates the database as though the updates arrived serially.
You may not care about durability for this application.
The mediation among the clients is, for my money, the most useful feature of the DBMS for your application. That will save you work, and headaches. Another, non-obvious, benefit is that it can enforce consistency rules for the state information; that can be remarkably useful to prevent an obsolete/corrupt client from munging the shared state.
The second part of your problem statement is notifying 2-10 clients of changed state. There are any number of ways to do that.
Some DBMSs can access OS services from triggers. You could have an update trigger issue a notification. Alternatively, the updating client could do that.
The actual notification mechanism could be quite simple. Clients could connect to a server (that you write) and block on read(2). The server itself listens on a port for update notifications. On receipt of one, it repeats it to all connected clients. When the client's read request returns, it's time to query the database for the updated state, and post a new read.
To prevent a kind of "thundering herd" problem when several updates arrive back-to-back, when a client reads the update message, it could keep reading updates until EWOULDBLOCK, and only then query the DBMS. OTOH, if it's important to see the intermediate states (to see every update, not just the current state), the DBMS is perfectly capable of storing and providing all versions and distinguishing them with a timestamp or serial number.
If you don't want to use TCP sockets directly, you might prefer ZeroMQ.
In this design, each client has three connections: the DBMS, the read-notify socket, and (maybe) the server-notify socket. The server has N+1 connections, for N clients and one listening socket. You have no locks to implement, very little tracking of participation, no problems re-synchronizing, and short windows inconsistency among clients as each one acts on its notification.
I have been recently looking into event sourcing and have some questions about the interactions with clients.
So event-sourcing sounds great. decoupling all your microservices, keeping your information in immutable events and formulating a stored states off of that to fit your needs is really handy. Having event propagate through your system/services and reacting to events in their own way is all fine.
The issue i am having lies with understanding the client interaction.
So you want clients to interact with the system, but they need to do this now by events. They can not longer submit a state to mutate your existing one.
So the question is how do clients fire off specific event and interact with (not only an event based system) but a system based on event sourcing.
My understanding is that you no longer use the rest api as resources (which you can get, update, delete, etc.. handling them as a resource), but you instead post to an endpoint as an event.
So how do these endpoint work?
my second question is how does the user get responses back?
for instance lets say we have an event to place an order.
your going to fire off an event an its going to do its thing. Again my understanding is that you dont now validate the request, e.g. checking if the user ordering the order has enough money, but instead fire it to be place and it will be handled in the system.
e.g. it will not be
- order placed
- this will be picked up by the pricing service and it will either fire an reserved money or money exceeded event based on if the user can afford it.
- The order service will then listen for those and then mark the order as denied or not enough credit.
So because this is a async process and the user has fired and forgotten, how do you then show the user it has either failed or succeeded? do you show them an order confirmation page with the order status as it is (even if its pending)
or do you poll it until it changes (web sockets or something).
I'm sorry if a lot of this is all nonsense, I am still learning about this architecture and am very much in the mindset of a monolith with REST responses.
Any help would be appreciated.
The issue i am having lies with understanding the client interaction.
Some of the issue may be understanding, but I promise you a fair share of the issue is that the literature sucks.
In particular, the word "Event" gets re-used a lot of different ways. If you aren't paying very careful attention to which meaning is being used, you are going to get knotted.
Event Sourcing is really about persistence - how does a micro-server store its private copy of state for later re-use? Instead of destructively overwriting our previous state, we write new information that links back to the previous state. If you imagine each microservice storing each change of state as a commit in its own git repository, you are in the right ballpark.
That's a different animal from using Event Messages to communicate information between one microservice and another.
There's some obvious overlap, of course, because the one message that you are likely to share with other microservices is "I just changed state".
So how do these endpoint work?
The same way that web forms do. I send you a representation of a form, the client displays the form to you. You fill in your data and submit the form, the client processes the contents of the form, and sends back to me an HTTP request with a "FormSubmitted" event in the message body.
You can achieve similar results by sending new representations of the state, but its a bit error prone to strip away the semantic intent and then try to guess it again on the server. So you are more likely to instead see task based user interfaces, or protocols that clearly identify the semantics of the change.
When the outside world is the authority for some piece of data (a shopper's shipping address, for example), you are more likely to see the more traditional "just edit the existing representation" approach.
So because this is a async process and the user has fired and forgotten, how do you then show the user it has either failed or succeeded?
Fire and forget really doesn't work for a distributed protocol on an unreliable network. In most cases, at-least-once delivery is important, so Fire until verified is the more common option. The initial acknowledgement of the message might be something like 202 Accepted -- "We received your message, we wrote it down, here's our current progress, here are some links you can fetch for progress reports".
It doesnt seem to me that event-sourcing fits with the traditional REST model where you CRUD a resource.
Jim Webber's 2011 talk may help to prune away the noise. A REST API is a disguise that your domain model wears; you exchange messages about manipulating resources, and as a side effect your domain model does useful work.
One way you could do this that would look more "traditional" is to work with representations of the event stream. I do a GET /08ff2ec9-a9ad-4be2-9793-18e232dbe615 and it returns me a representation of a list of events. I append a new event onto the end of that list, and PUT /08ff2ec9-a9ad-4be2-9793-18e232dbe615, and interesting side effects happen. Or perhaps I instead create a patch document that describes my change, and PATCH /08ff2ec9-a9ad-4be2-9793-18e232dbe615.
But more likely, I would do something else -- instead of GET /08ff2ec9-a9ad-4be2-9793-18e232dbe615 to fetch a representation of the list of events, I'd probably GET /08ff2ec9-a9ad-4be2-9793-18e232dbe615 to fetch a representation of available protocols - which is to say, a document filled with hyper links. From there, I might GET /08ff2ec9-a9ad-4be2-9793-18e232dbe615/603766ac-92af-47f3-8265-16f003ce5a09 to obtain a representation of the data collection form. I fill in the details of my event, submit the form, and POST /08ff2ec9-a9ad-4be2-9793-18e232dbe615 the form data to the server.
You can, of course, use any spelling you like for the URI.
In the first case, we need something like an HTTP capable document editor; the second case uses something more like a web browser.
If there were lots of different kinds of events, then the second case might well have lots of different form resources, all submitting POST /08ff2ec9-a9ad-4be2-9793-18e232dbe615 requests.
(You don't have to have all of the forms submitting to the same URI, but there are advantages to consider).
In a non event sourcing pattern I guess that would be first put into the database, then the event gets risen.
Even when you aren't event sourcing, there may still be some advantages to committing events to your durable store before emitting them. See Pat Helland: Data on the Outside versus Data on the Inside.
So you want clients to interact with the system, but they need to do this now by events.
Clients don't have to. Client may even not be aware of the underlying event store.
There are a number of trade-offs to consider and decisions to take when implementing an event-sourced system. To start with you can try to name a few pre computer era examples of event-sourced systems and look at their non-functional characteristics.
So the question is how do clients fire off specific event
Clients don't send events. They rather should express an intent (a command). Then it is the responsibility of the event-sourced system to validate the intent and either reject it or accept and store the corresponding event. It would mean that an intent to change the system's state was accepted and the stored event confirms the change.
My understanding is that you no longer use the rest api as resources
REST is one of the options. You just consider different things as resources. A command can be a REST resource. An event-sourced entity can be a resource, to which you POST a command. If you like it async - you can later GET the command to check its status. You can GET an entity to know its current state. You cant GET events from a class of entities as a means of subscription.
If we are talking about an end user, then most likely it doesn't deal with the event store directly. There is some third tier in between, which does CQRS. From a user client perspective it can be provided with REST, GraphQL, SOAP, gRPC or event e-mail. Whatever transport solution you find suitable. Command-processing part from CQRS is what specifically domain-driven. It decides which intent to accept and which to reject.
Event store itself is responsible for the data consistency. I.e. it should not allow two concurrent event leading to invalid state be published. This is what pre-computer event-sourced systems are good at. You usually have some physical object as an entity, so you lock for update by just getting hand of it.
Then an end-user client usually reads from some prepared read model. The responsibility of a read (R in CQRS) component is to prepare read-optimised data for clients. This data may come from multiple event-sourced of the same or different classes. Again, client may interact with a read model with whatever transport is suitable.
While an event-store is consistent and consistent immediately, a read model is eventually consistent. But it's up to you to tune this eventuality.
Just try to throw REST out of the architecture for a while. Consider it a one of available transport options - that may help to look at the root.
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I am thinking about buildning a REST API with both websockets and http where I use websockets to tell the client that new data is available or provide the new data to the client directly.
Here are some different ideas of how it could work:
ws = websocket
Idea A:
David get all users with GET /users
Jacob add a user with POST /users
A ws message is sent to all clients with info that a new user exist
David recive a message by ws and calls GET /users
Idea B:
David get all users with GET /users
David register to get ws updates when a change is done to /users
Jacob add a user with POST /users
The new user is sent to David by ws
Idea C:
David get all users with GET /users
David register to get ws updates when a change is done to /users
Jacob add a user with POST /users and it gets the id 4
David receive the id 4 of the new user by ws
David get the new user with GET /users/4
Idea D:
David get all users with GET /users
David register to get ws updates when changes is done to /users.
Jacob add a user with POST /users
David receive a ws message that changes is done to /users
David get only the delta by calling GET /users?lastcall='time of step one'
Which alternative is the best and what are the pros and cons?
Is it another better 'Idea E'?
Do we even need to use REST or is ws enought for all data?
Edit
To solve problems with data getting out of sync we could provide the header"If-Unmodified-Since"https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/If-Unmodified-Sinceor "E-Tag" https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/ETag or both with PUT requests.
Idea B is for me the best, because the client specifically subscribes for changes in a resource, and gets the incremental updates from that moment.
Do we even need to use REST or is ws enought for all data?
Please check: WebSocket/REST: Client connections?
I don't know Java, but I worked with both Ruby and C on these designs...
Funny enough, I think the easiest solution is to use JSON, where the REST API simply adds the method data (i.e. method: "POST") to the JSON and forwards the request to the same handler the Websocket uses.
The underlying API's response (the response from the API handling JSON requests) can be translated to any format you need, such as HTML rendering... though I would consider simply returning JSON for most use cases.
This helps encapsulate the code and keep it DRY while accessing the same API using both REST and Websockets.
As you might infer, this design makes testing easier, since the underlying API that handles the JSON can be tested locally without the need to emulate a server.
Good Luck!
P.S. (Pub/Sub)
As for the Pub/Sub, I find it best to have a "hook" for any update API calls (a callback) and a separate Pub/Sub module that handles these things.
I also find it more resource friendly to write the whole data to the Pub/Sub service (option B) instead of just a reference number (option C) or an "update available" message (options A and D).
In general, I also believe that sending the whole user list isn't effective for larger systems. Unless you have 10-15 users, the database call might be a bust. Consider the Amazon admin calling for a list of all users... Brrr....
Instead, I would consider dividing this to pages, say 10-50 users a page. These tables can be filled using multiple requests (Websocket / REST, doesn't matter) and easily updated using live Pub/Sub messages or reloaded if a connection was lost and reestablished.
EDIT (REST vs. Websockets)
As For REST vs. Websockets... I find the question of need is mostly a subset of the question "who's the client?"...
However, once the logic is separated from the transport layer, than supporting both is very easy and often it makes more sense to support both.
I should note that Websockets often have a slight edge when it comes to authentication (credentials are exchanged once per connection instead of once per request). I don't know if this is a concern.
For the same reason (as well as others), Websockets usually have an edge with regards to performance... how big an edge over REST depends on the REST transport layer (HTTP/1.1, HTTP/2, etc').
Usually these things are negligible when it comes time to offer a public API access point and I believe implementing both is probably the way to go for now.
To summarize your ideas:
A: Send a message to all clients when a user edits data on the server. All users then request an update of all data.
-This system may make a lot of unnecessary server calls on behalf of clients who are not using the data. I don't recommend producing all of that extra traffic as processing and sending those updates could become costly.
B: After a user pulls data from the server, they then subscribe to updates from the server which sends them information about what has changed.
-This saves a lot of server traffic, but if you ever get out of sync, you're going to be posting incorrect data to your users.
C: Users who subscribe to data updates are sent information about which data has been updated, then fetch it again themselves.
-This is the worst of A and B in that you'll have extra round trips between your users and servers just to notify them that they need to make a request for information which may be out of sync.
D: Users who subscribe to updates are notified when any changes are made and then request the last change made to the server.
-This presents all of the problems with C, but includes the possibility that, once out of sync, you may send data that will be nonsense to your users which might just crash the client side app for all we know.
I think that this option E would be best:
Every time data changes on the server, send the contents of all the data to the clients who have subscribed to it. This limits the traffic between your users and the server while also giving them the least chance of having out of sync data. They might get stale data if their connection drops, but at least you wouldn't be sending them something like Delete entry 4 when you aren't sure whether or not they got the message that entry 5 just moved into slot 4.
Some Considerations:
How often does the data get updated?
How many users need to be updated each time an update occurs?
What are your transmission
costs? If you have users on mobile devices with slow connections, that will affect how often and how much you can afford to send to them.
How much data gets updated in a given update?
What happens if a user sees stale data?
What happens if a user gets data out of sync?
Your worst case scenario would be something like this: Lots of users, with slow connections who are frequently updating large amounts of data that should never be stale and, if it gets out of sync, becomes misleading.
I personally have used Idea B in production and am very satisfied with the results. We use http://www.axonframework.org/, so every change or creation of an entity is published as an event throughout the application. These events are then used to update several read models, which are basically simple Mysql tables backing one or more queries. I added some interceptors to the event processors that update these read models so that they publish the events they just processed after the data is committed to the DB.
Publishing of events is done through STOMP over web sockets. It is made very simple is you use Spring's Web Socket support (https://docs.spring.io/spring/docs/current/spring-framework-reference/html/websocket.html). This is how I wrote it:
#Override
protected void dispatch(Object serializedEvent, String topic, Class eventClass) {
Map<String, Object> headers = new HashMap<>();
headers.put("eventType", eventClass.getName());
messagingTemplate.convertAndSend("/topic" + topic, serializedEvent, headers);
}
I wrote a little configurer that uses Springs bean factory API so that I can annotate my Axon event handlers like this:
#PublishToTopics({
#PublishToTopic(value = "/salary-table/{agreementId}/{salaryTableId}", eventClass = SalaryTableChanged.class),
#PublishToTopic(
value = "/salary-table-replacement/{agreementId}/{activatedTable}/{deactivatedTable}",
eventClass = ActiveSalaryTableReplaced.class
)
})
Of course, that is just one way to do it. Connecting on the client side may look something like this:
var connectedClient = $.Deferred();
function initialize() {
var basePath = ApplicationContext.cataDirectBaseUrl().replace(/^https/, 'wss');
var accessToken = ApplicationContext.accessToken();
var socket = new WebSocket(basePath + '/wss/query-events?access_token=' + accessToken);
var stompClient = Stomp.over(socket);
stompClient.connect({}, function () {
connectedClient.resolve(stompClient);
});
}
this.subscribe = function (topic, callBack) {
connectedClient.then(function (stompClient) {
stompClient.subscribe('/topic' + topic, function (frame) {
callBack(frame.headers.eventType, JSON.parse(frame.body));
});
});
};
initialize();
Another option is to use Firebase Cloud Messaging:
Using FCM, you can notify a client app that new email or other data is
available to sync.
How does it work?
An FCM implementation includes two main components for sending and
receiving:
A trusted environment such as Cloud Functions for Firebase or an app server on which to build, target and send messages.
An iOS, Android, or Web (JavaScript) client app that receives messages.
Client registers its Firebase key to a server. When updates are available, server sends push notification to the Firebase key associated with the client. Client may receive data in notification structure or sync it with a server after receiving a notification.
Generally you might have a look at current "realtime" web frameworks like MeteorJS which tackle exactly this problem.
Meteor in specific works more or less like your example D with subscriptions on certain data and deltas being sent out after changes only to the affected clients. Their protocol used is called DDP which additionally sends the deltas not as overhead prone HTML but raw data.
If websockets are not available fallbacks like long polling or server sent events can be used.
If you plan to implement it yourself i hope these sources are some kind of inspiration how this problem has been approached. As already stated the specific use case is important
The answer depends on your use case. For the most part though I've found that you can implement everything you need with sockets. As long as you are only trying to access your server with clients who can support sockets. Also, scale can be an issue when you're using only sockets. Here are some examples of how you could use just sockets.
Server side:
socket.on('getUsers', () => {
// Get users from db or data model (save as user_list).
socket.emit('users', user_list );
})
socket.on('createUser', (user_info) => {
// Create user in db or data model (save created user as user_data).
io.sockets.emit('newUser', user_data);
})
Client side:
socket.on('newUser', () => {
// Get users from db or data model (save as user_list).
socket.emit('getUsers');
})
socket.on('users', (users) => {
// Do something with users
})
This uses socket.io for node. I'm not sure what your exact scenario is but this would work for that case. If you need to include REST endpoints that would be fine too.
With all great information all the great people added before me.
I found that eventually there is no right or wrong, its simply goes down to what suits your needs:
lets take CRUD in this scenario:
WS Only Approach:
Create/Read/Update/Deleted information goes all through the websocket.
--> e.g If you have critical performance considerations ,that is not
acceptable that the web client will do successive REST request to fetch
information,or if you know that you want the whole data to be seen in
the client no matter what was the event , so just send the CRUD events
AND DATA inside the websocket.
WS TO SEND EVENT INFO + REST TO CONSUME THE DATA ITSELF
Create/Read/Update/Deleted , Event information is sent in the Websocket,
giving the web client information that is necessary to send the proper
REST request to fetch exactly the thing the CRUD that happend in server.
e.g. WS sends UsersListChangedEvent {"ListChangedTrigger: "ItemModified" , "IdOfItem":"XXXX#3232" , "UserExtrainformation":" Enough info to let the client decide if it relevant for it to fetch the changed data"}
I found that using WS [Only for using Event Data] and REST
[To consume the data ]is better because:
[1] Separation between reading and writing model, Imagine you want to add some runtime information when your data is retrieved when its read from REST , that is now achieved because you are not mixing Write & Read models like in 1.
[2] Lets say other platform , not necessarily web client will consume this data.
so you just change the Event trigger from WS to the new way, and use REST to
consume the data.
[3] Client do not need to write 2 ways to read the new/modified data.
usually there is also code that reads the data when the page loads , and not
through the websocket , this code now can be used twice , once when page
loads , and second when WS triggered the specific event.
[4] Maybe the client do not want to fetch the new User because its showing currently only a view of old Data[E.g. users] , and new data changes is not in its interest to fetch ?
i prefer the A, it allows client the flexibility whether or not to update the existing data.
also with this method, implementation and access control becomes much more easier.
for example you can simply broadcast the userUpdated event to all users, this saves having a client list for do specific broadcasts and the Access Controls and Authentications applied for your REST Route wont have to change to reapplied again because the client is gonna make a GET request again.
Many things depends on the what kind of application you are making.
I'm wondering how you'd implement the following use-case in REST. Is it even possible to do without compromising the conceptual model?
Read or update multiple resources within the scope of a single transaction. For example, transfer $100 from Bob's bank account into John's account.
As far as I can tell, the only way to implement this is by cheating. You could POST to the resource associated with either John or Bob and carry out the entire operation using a single transaction. As far as I'm concerned this breaks the REST architecture because you're essentially tunneling an RPC call through POST instead of really operating on individual resources.
Consider a RESTful shopping basket scenario. The shopping basket is conceptually your transaction wrapper. In the same way that you can add multiple items to a shopping basket and then submit that basket to process the order, you can add Bob's account entry to the transaction wrapper and then Bill's account entry to the wrapper. When all the pieces are in place then you can POST/PUT the transaction wrapper with all the component pieces.
There are a few important cases that aren't answered by this question, which I think is too bad, because it has a high ranking on Google for the search terms :-)
Specifically, a nice propertly would be: If you POST twice (because some cache hiccupped in the intermediate) you should not transfer the amount twice.
To get to this, you create a transaction as an object. This could contain all the data you know already, and put the transaction in a pending state.
POST /transfer/txn
{"source":"john's account", "destination":"bob's account", "amount":10}
{"id":"/transfer/txn/12345", "state":"pending", "source":...}
Once you have this transaction, you can commit it, something like:
PUT /transfer/txn/12345
{"id":"/transfer/txn/12345", "state":"committed", ...}
{"id":"/transfer/txn/12345", "state":"committed", ...}
Note that multiple puts don't matter at this point; even a GET on the txn would return the current state. Specifically, the second PUT would detect that the first was already in the appropriate state, and just return it -- or, if you try to put it into the "rolledback" state after it's already in "committed" state, you would get an error, and the actual committed transaction back.
As long as you talk to a single database, or a database with an integrated transaction monitor, this mechanism will actually work just fine. You might additionally introduce time-outs for transactions, which you could even express using Expires headers if you wanted to.
In REST terms, resources are nouns that can be acted on with CRUD (create/read/update/delete) verbs. Since there is no "transfer money" verb, we need to define a "transaction" resource that can be acted upon with CRUD. Here's an example in HTTP+POX. First step is to CREATE (HTTP POST method) a new empty transaction:
POST /transaction
This returns a transaction ID, e.g. "1234" and according URL "/transaction/1234". Note that firing this POST multiple times will not create the same transaction with multiple IDs and also avoids introduction of a "pending" state. Also, POST can't always be idempotent (a REST requirement), so it's generally good practice to minimize data in POSTs.
You could leave the generation of a transaction ID up to the client. In this case, you would POST /transaction/1234 to create transaction "1234" and the server would return an error if it already existed. In the error response, the server could return a currently unused ID with an appropriate URL. It's not a good idea to query the server for a new ID with a GET method, since GET should never alter server state, and creating/reserving a new ID would alter server state.
Next up, we UPDATE (PUT HTTP method) the transaction with all data, implicitly committing it:
PUT /transaction/1234
<transaction>
<from>/account/john</from>
<to>/account/bob</to>
<amount>100</amount>
</transaction>
If a transaction with ID "1234" has been PUT before, the server gives an error response, otherwise an OK response and a URL to view the completed transaction.
NB: in /account/john , "john" should really be John's unique account number.
Great question, REST is mostly explained with database-like examples, where something is stored, updated, retrieved, deleted. There are few examples like this one, where the server is supposed to process the data in some way. I don't think Roy Fielding included any in his thesis, which was based on http after all.
But he does talk about "representational state transfer" as a state machine, with links moving to the next state. In this way, the documents (the representations) keep track of the client state, instead of the server having to do it. In this way, there is no client state, only state in terms of which link you are on.
I've been thinking about this, and it seems to me reasonable that to get the server to process something for you, when you upload, the server would automatically create related resources, and give you the links to them (in fact, it wouldn't need to automatically create them: it could just tell you the links, and it only create them when and if you follow them - lazy creation). And to also give you links to create new related resources - a related resource has the same URI but is longer (adds a suffix). For example:
You upload (POST) the representation of the concept of a transaction with all the information. This looks just like a RPC call, but it's really creating the "proposed transaction resource". e.g URI: /transaction
Glitches will cause multiple such resources to be created, each with a different URI.
The server's response states the created resource's URI, its representation - this includes the link (URI) to create the related resource of a new "committed transaction resource". Other related resources are the link to delete the proposed transaction. These are states in the state-machine, which the client can follow. Logically, these are part of the resource that has been created on the server, beyond the information the client supplied. e.g URIs: /transaction/1234/proposed, /transaction/1234/committed
You POST to the link to create the "committed transaction resource", which creates that resource, changing the state of the server (the balances of the two accounts)**. By its nature, this resource can only be created once, and can't be updated. Therefore, glitches committing many transactions can't occur.
You can GET those two resources, to see what their state is. Assuming that a POST can change other resources, the proposal would now be flagged as "committed" (or perhaps, not available at all).
This is similar to how webpages operate, with the final webpage saying "are you sure you want to do this?" That final webpage is itself a representation of the state of the transaction, which includes a link to go to the next state. Not just financial transactions; also (eg) preview then commit on wikipedia. I guess the distinction in REST is that each stage in the sequence of states has an explicit name (its URI).
In real-life transactions/sales, there are often different physical documents for different stages of a transaction (proposal, purchase order, receipt etc). Even more for buying a house, with settlement etc.
OTOH This feels like playing with semantics to me; I'm uncomfortable with the nominalization of converting verbs into nouns to make it RESTful, "because it uses nouns (URIs) instead of verbs (RPC calls)". i.e. the noun "committed transaction resource" instead of the verb "commit this transaction". I guess one advantage of nominalization is you can refer to the resource by name, instead of needing to specify it in some other way (such as maintaining session state, so you know what "this" transaction is...)
But the important question is: What are the benefits of this approach? i.e. In what way is this REST-style better than RPC-style? Is a technique that's great for webpages also helpful for processing information, beyond store/retrieve/update/delete? I think that the key benefit of REST is scalability; one aspect of that is not needing to maintain client state explicitly (but making it implicit in the URI of the resource, and the next states as links in its representation). In that sense it helps. Perhaps this helps in layering/pipelining too? OTOH only the one user will look at their specific transaction, so there's no advantage in caching it so others can read it, the big win for http.
I've drifted away from this topic for 10 years. Coming back, I can't believe the religion masquerading as science that you wade into when you google rest+reliable. The confusion is mythic.
I would divide this broad question into three:
Downstream services. Any web service you develop will have downstream services that you use, and whose transaction syntax you have no choice but to follow. You should try and hide all this from users of your service, and make sure all parts of your operation succeed or fail as a group, then return this result to your users.
Your services. Clients want unambiguous outcomes to web-service calls, and the usual REST pattern of making POST, PUT or DELETE requests directly on substantive resources strikes me as a poor, and easily improved, way of providing this certainty. If you care about reliability, you need to identify action requests. This id can be a guid created on the client, or a seed value from a relational DB on the server, it doesn't matter. For server generated ID's, use a 'preflight' request-response to exchange the id of the action. If this request fails or half succeeds, no problem, the client just repeats the request. Unused ids do no harm.This is important because it lets all subsequent requests be fully idempotent, in the sense that if they are repeated n times they return the same result and cause nothing further to happen. The server stores all responses against the action id, and if it sees the same request, it replays the same response. A fuller treatment of the pattern is in this google doc. The doc suggests an implementation that, I believe(!), broadly follows REST principals. Experts will surely tell me how it violates others. This pattern can be usefully employed for any unsafe call to your web-service, whether or not there are downstream transactions involved.
Integration of your service into "transactions" controlled by upstream services. In the context of web-services, full ACID transactions are considered as usually not worth the effort, but you can greatly help consumers of your service by providing cancel and/or confirm links in your confirmation response, and thus achieve transactions by compensation.
Your requirement is a fundamental one. Don't let people tell you your solution is not kosher. Judge their architectures in the light of how well, and how simply, they address your problem.
If you stand back to summarize the discussion here, it's pretty clear that REST is not appropriate for many APIs, particularly when the client-server interaction is inherently stateful, as it is with non-trivial transactions. Why jump through all the hoops suggested, for client and server both, in order to pedantically follow some principle that doesn't fit the problem? A better principle is to give the client the easiest, most natural, productive way to compose with the application.
In summary, if you're really doing a lot of transactions (types, not instances) in your application, you really shouldn't be creating a RESTful API.
You'd have to roll your own "transaction id" type of tx management. So it would be 4 calls:
http://service/transaction (some sort of tx request)
http://service/bankaccount/bob (give tx id)
http://service/bankaccount/john (give tx id)
http://service/transaction (request to commit)
You'd have to handle the storing of the actions in a DB (if load balanced) or in memory or such, then handling commit, rollback, timeout.
Not really a RESTful day in the park.
First of all transferring money is nothing that you can not do in a single resource call. The action you want to do is sending money. So you add a money transfer resource to the account of the sender.
POST: accounts/alice, new Transfer {target:"BOB", abmount:100, currency:"CHF"}.
Done. You do not need to know that this is a transaction that must be atomic etc. You just transfer money aka. send money from A to B.
But for the rare cases here a general solution:
If you want to do something very complex involving many resources in a defined context with a lot of restrictions that actually cross the what vs. why barrier (business vs. implementation knowledge) you need to transfer state. Since REST should be stateless you as a client need to transfer the state around.
If you transfer state you need to hide the information inside from the client. The client should not know internal information only needed by the implementation but does not carry information relevant in terms of business. If those information have no business value the state should be encrypted and a metaphor like token, pass or something need to be used.
This way one can pass internal state around and using encryption and signing the system can be still be secure and sound. Finding the right abstraction for the client why he passes around state information is something that is up to the design and architecture.
The real solution:
Remember REST is talking HTTP and HTTP comes with the concept of using cookies. Those cookies are often forgotten when people talk about REST API and workflows and interactions spanning multiple resources or requests.
Remember what is written in the Wikipedia about HTTP cookies:
Cookies were designed to be a reliable mechanism for websites to remember stateful information (such as items in a shopping cart) or to record the user's browsing activity (including clicking particular buttons, logging in, or recording which pages were visited by the user as far back as months or years ago).
So basically if you need to pass on state, use a cookie. It is designed for exactly the very same reason, it is HTTP and therefore it is compatible to REST by design :).
The better solution:
If you talk about a client performing a workflow involving multiple requests you usually talk about protocol. Every form of protocol comes with a set of preconditions for each potential step like perform step A before you can do B.
This is natural but exposing protocol to clients makes everything more complex. In order to avoid it just think what we do when we have to do complex interactions and things in the real world... . We use an Agent.
Using the Agent metaphor you can provide a resource that can perform all necessary steps for you and store the actual assignment / instructions it is acting upon in its list (so we can use POST on the agent or an 'agency').
A complex example:
Buying a house:
You need to prove your credibility (like providing your police record entries), you need to ensure financial details, you need to buy the actual house using a lawyer and a trusted third party storing the funds, verify that the house now belongs to you and add the buying stuff to your tax records etc. (just as an example, some steps may be wrong or whatever).
These steps might take several days to be completed, some can be done in parallel etc.
In order to do this, you just give the agent the task buy house like:
POST: agency.com/ { task: "buy house", target:"link:toHouse", credibilities:"IamMe"}.
Done. The agency sends you back a reference to you that you can use to see and track the status of this job and the rest is done automatically by the agents of the agency.
Think about a bug tracker for instance. Basically you report the bug and can use the bug id to check whats going on. You can even use a service to listen to changes of this resource. Mission Done.
You must not use server side transactions in REST.
One of the REST contraints:
Stateless
The client–server communication is further constrained by no client context being stored on the server between requests. Each request from any client contains all of the information necessary to service the request, and any session state is held in the client.
The only RESTful way is to create a transaction redo log and put it into the client state. With the requests the client sends the redo log and the server redoes the transaction and
rolls the transaction back but provides a new transaction redo log (one step further)
or finally complete the transaction.
But maybe it's simpler to use a server session based technology which supports server side transactions.
I think that in this case it is totally acceptable to break the pure theory of REST in this situation. In any case, I don't think there is anything actually in REST that says you can't touch dependent objects in business cases that require it.
I really think it's not worth the extra hoops you would jump through to create a custom transaction manager, when you could just leverage the database to do it.
In the simple case (without distributed resources), you could consider the transaction as a resource, where the act of creating it attains the end objective.
So, to transfer between <url-base>/account/a and <url-base>/account/b, you could post the following to <url-base>/transfer.
<transfer>
<from><url-base>/account/a</from>
<to><url-base>/account/b</to>
<amount>50</amount>
</transfer>
This would create a new transfer resource and return the new url of the transfer - for example <url-base>/transfer/256.
At the moment of successful post, then, the 'real' transaction is carried out on the server, and the amount removed from one account and added to another.
This, however, doesn't cover a distributed transaction (if, say 'a' is held at one bank behind one service, and 'b' is held at another bank behind another service) - other than to say "try to phrase all operations in ways that don't require distributed transactions".
I believe that would be the case of using a unique identifier generated on the client to ensure that the connection hiccup not imply in an duplicity saved by the API.
I think using a client generated GUID field along with the transfer object and ensuring that the same GUID was not reinserted again would be a simpler solution to the bank transfer matter.
Do not know about more complex scenarios, such as multiple airline ticket booking or micro architectures.
I found a paper about the subject, relating the experiences of dealing with the transaction atomicity in RESTful services.
I guess you could include the TAN in the URL/resource:
PUT /transaction to get the ID (e.g. "1")
[PUT, GET, POST, whatever] /1/account/bob
[PUT, GET, POST, whatever] /1/account/bill
DELETE /transaction with ID 1
Just an idea.